Split (2)

train · 433k rows

id stringlengths 9 16	title stringlengths 4 278	abstract stringlengths 3 4.08k	cs.HC bool 2 classes	cs.CE bool 2 classes	cs.SD bool 2 classes	cs.SI bool 2 classes	cs.AI bool 2 classes	cs.IR bool 2 classes	cs.LG bool 2 classes	cs.RO bool 2 classes	cs.CL bool 2 classes	cs.IT bool 2 classes	cs.SY bool 2 classes	cs.CV bool 2 classes	cs.CR bool 2 classes	cs.CY bool 2 classes	cs.MA bool 2 classes	cs.NE bool 2 classes	cs.DB bool 2 classes	Other bool 2 classes	__index_level_0__ int64 0 541k
2009.09936	Prune Responsibly	Irrespective of the specific definition of fairness in a machine learning application, pruning the underlying model affects it. We investigate and document the emergence and exacerbation of undesirable per-class performance imbalances, across tasks and architectures, for almost one million categories considered across ...	false	false	false	false	false	false	true	false	false	false	false	true	false	true	false	false	false	false	196,746
1504.07495	Decode-Forward Transmission for the Two-Way Relay Channels	We propose composite decode-forward (DF) schemes for the two-way relay channel in both the full- and half-duplex modes by combining coherent relaying, independent relaying and partial relaying strategies. For the full-duplex mode, the relay partially decodes each user's information in each block and forwards this parti...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	42,545
2105.02711	SafeDrug: Dual Molecular Graph Encoders for Recommending Effective and Safe Drug Combinations	Medication recommendation is an essential task of AI for healthcare. Existing works focused on recommending drug combinations for patients with complex health conditions solely based on their electronic health records. Thus, they have the following limitations: (1) some important data such as drug molecule structures h...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	233,899
1811.07161	Edge-Based Blur Kernel Estimation Using Sparse Representation and Self-Similarity	Blind image deconvolution is the problem of recovering the latent image from the only observed blurry image when the blur kernel is unknown. In this paper, we propose an edge-based blur kernel estimation method for blind motion deconvolution. In our previous work, we incorporate both sparse representation and self-simi...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	113,691
1301.6196	On the Number of Interference Alignment Solutions for the K-User MIMO Channel with Constant Coefficients	In this paper, we study the number of different interference alignment (IA) solutions in a K-user multiple-input multiple-output (MIMO) interference channel, when the alignment is performed via beamforming and no symbol extensions are allowed. We focus on the case where the number of IA equations matches the number of ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	21,392
2112.07869	Fine-Tuning Large Neural Language Models for Biomedical Natural Language Processing	Motivation: A perennial challenge for biomedical researchers and clinical practitioners is to stay abreast with the rapid growth of publications and medical notes. Natural language processing (NLP) has emerged as a promising direction for taming information overload. In particular, large neural language models facilita...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	271,613
1103.3952	Mixing, Ergodic, and Nonergodic Processes with Rapidly Growing Information between Blocks	We construct mixing processes over an infinite alphabet and ergodic processes over a finite alphabet for which Shannon mutual information between adjacent blocks of length $n$ grows as $n^\beta$, where $\beta\in(0,1)$. The processes are a modification of nonergodic Santa Fe processes, which were introduced in the conte...	false	false	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	9,692
2410.19003	Whither Bias Goes, I Will Go: An Integrative, Systematic Review of Algorithmic Bias Mitigation	Machine learning (ML) models are increasingly used for personnel assessment and selection (e.g., resume screeners, automatically scored interviews). However, concerns have been raised throughout society that ML assessments may be biased and perpetuate or exacerbate inequality. Although organizational researchers have b...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	502,131
2310.15161	SAM-Med3D: Towards General-purpose Segmentation Models for Volumetric Medical Images	Existing volumetric medical image segmentation models are typically task-specific, excelling at specific target but struggling to generalize across anatomical structures or modalities. This limitation restricts their broader clinical use. In this paper, we introduce SAM-Med3D for general-purpose segmentation on volumet...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	402,197
2109.04242	IICNet: A Generic Framework for Reversible Image Conversion	Reversible image conversion (RIC) aims to build a reversible transformation between specific visual content (e.g., short videos) and an embedding image, where the original content can be restored from the embedding when necessary. This work develops Invertible Image Conversion Net (IICNet) as a generic solution to vari...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	254,327
2207.04692	PUF-Phenotype: A Robust and Noise-Resilient Approach to Aid Intra-Group-based Authentication with DRAM-PUFs Using Machine Learning	As the demand for highly secure and dependable lightweight systems increases in the modern world, Physically Unclonable Functions (PUFs) continue to promise a lightweight alternative to high-cost encryption techniques and secure key storage. While the security features promised by PUFs are highly attractive for secure ...	false	false	false	false	false	false	false	false	false	false	false	true	true	false	false	false	false	false	307,282
1709.07758	Improving Language Modelling with Noise-contrastive estimation	Neural language models do not scale well when the vocabulary is large. Noise-contrastive estimation (NCE) is a sampling-based method that allows for fast learning with large vocabularies. Although NCE has shown promising performance in neural machine translation, it was considered to be an unsuccessful approach for lan...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	81,334
2004.06830	Differentially Private Assouad, Fano, and Le Cam	Le Cam's method, Fano's inequality, and Assouad's lemma are three widely used techniques to prove lower bounds for statistical estimation tasks. We propose their analogues under central differential privacy. Our results are simple, easy to apply and we use them to establish sample complexity bounds in several estimatio...	false	false	false	false	false	false	true	false	false	true	false	false	true	false	false	false	false	true	172,611
2011.08985	A User's Guide to Calibrating Robotics Simulators	Simulators are a critical component of modern robotics research. Strategies for both perception and decision making can be studied in simulation first before deployed to real world systems, saving on time and costs. Despite significant progress on the development of sim-to-real algorithms, the analysis of different met...	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	207,050
2302.01563	Causal Inference Based Single-branch Ensemble Trees For Uplift Modeling	In this manuscript (ms), we propose causal inference based single-branch ensemble trees for uplift modeling, namely CIET. Different from standard classification methods for predictive probability modeling, CIET aims to achieve the change in the predictive probability of outcome caused by an action or a treatment. Accor...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	343,659
2406.05559	ThatiAR: Subjectivity Detection in Arabic News Sentences	Detecting subjectivity in news sentences is crucial for identifying media bias, enhancing credibility, and combating misinformation by flagging opinion-based content. It provides insights into public sentiment, empowers readers to make informed decisions, and encourages critical thinking. While research has developed m...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	462,185
2310.10061	A computational model of serial and parallel processing in visual search	The following is a dissertation aimed at understanding what the various phenomena in visual search teach us about the nature of human visual representations and processes. I first review some of the major empirical findings in the study of visual search. I next present a theory of visual search in terms of what I belie...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	400,069
1903.11719	Fairness in Algorithmic Decision Making: An Excursion Through the Lens of Causality	As virtually all aspects of our lives are increasingly impacted by algorithmic decision making systems, it is incumbent upon us as a society to ensure such systems do not become instruments of unfair discrimination on the basis of gender, race, ethnicity, religion, etc. We consider the problem of determining whether th...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	125,564
1510.07573	Generalized Regressive Motion: a Visual Cue to Collision	Brains and sensory systems evolved to guide motion. Central to this task is controlling the approach to stationary obstacles and detecting moving organisms. Looming has been proposed as the main monocular visual cue for detecting the approach of other animals and avoiding collisions with stationary obstacles. Elegant n...	false	false	false	false	false	false	false	true	false	false	true	true	false	false	true	false	false	false	48,216
2307.11672	Robust Feature Inference: A Test-time Defense Strategy using Spectral Projections	Test-time defenses are used to improve the robustness of deep neural networks to adversarial examples during inference. However, existing methods either require an additional trained classifier to detect and correct the adversarial samples, or perform additional complex optimization on the model parameters or the input...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	380,989
2310.12309	A Unifying Framework for Learning Argumentation Semantics	Argumentation is a very active research field of Artificial Intelligence concerned with the representation and evaluation of arguments used in dialogues between humans and/or artificial agents. Acceptability semantics of formal argumentation systems define the criteria for the acceptance or rejection of arguments. Seve...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	400,969
1912.05393	Fine-grained Classification of Rowing teams	Fine-grained classification tasks such as identifying different breeds of dog are quite challenging as visual differences between categories is quite small and can be easily overwhelmed by external factors such as object pose, lighting, etc. This work focuses on the specific case of classifying rowing teams from variou...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	157,095
2403.06100	Automatic design optimization of preference-based subjective evaluation with online learning in crowdsourcing environment	A preference-based subjective evaluation is a key method for evaluating generative media reliably. However, its huge combinations of pairs prohibit it from being applied to large-scale evaluation using crowdsourcing. To address this issue, we propose an automatic optimization method for preference-based subjective eval...	true	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	436,302
2309.01715	Prompting or Fine-tuning? A Comparative Study of Large Language Models for Taxonomy Construction	Taxonomies represent hierarchical relations between entities, frequently applied in various software modeling and natural language processing (NLP) activities. They are typically subject to a set of structural constraints restricting their content. However, manual taxonomy construction can be time-consuming, incomplete...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	389,778
2312.01118	Beyond Accuracy: Statistical Measures and Benchmark for Evaluation of Representation from Self-Supervised Learning	Recently, self-supervised metric learning has raised attention for the potential to learn a generic distance function. It overcomes the limitations of conventional supervised one, e.g., scalability and label biases. Despite progress in this domain, current benchmarks, incorporating a narrow scope of classes, stop the n...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	412,321
2204.06692	Stability of China's Stock Market: Measure and Forecast by Ricci Curvature on Network	The systemic stability of a stock market is one of the core issues in the financial field. The market can be regarded as a complex network whose nodes are stocks connected by edges that signify their correlation strength. Since the market is a strongly nonlinear system, it is difficult to measure the macroscopic stabil...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	291,424
1807.04109	Modeling and Soft-fault Diagnosis of Underwater Thrusters with Recurrent Neural Networks	Noncritical soft-faults and model deviations are a challenge for Fault Detection and Diagnosis (FDD) of resident Autonomous Underwater Vehicles (AUVs). Such systems may have a faster performance degradation due to the permanent exposure to the marine environment, and constant monitoring of component conditions is requi...	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	102,673
1409.2080	Multiscale statistical testing for connectome-wide association studies in fMRI	Alterations in brain connectivity have been associated with a variety of clinical disorders using functional magnetic resonance imaging (fMRI). We investigated empirically how the number of brain parcels (or scale) impacted the results of a mass univariate general linear model (GLM) on connectomes. The brain parcels us...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	35,878
2111.15634	RPS: Portfolio Asset Selection using Graph based Representation Learning	Portfolio optimization is one of the essential fields of focus in finance. There has been an increasing demand for novel computational methods in this area to compute portfolios with better returns and lower risks in recent years. We present a novel computational method called Representation Portfolio Selection (RPS) b...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	268,993
1312.7432	A network analysis of Sibiu County, Romania	Network science methods have proved to be able to provide useful insights from both a theoretical and a practical point of view in that they can better inform governance policies in complex dynamic environments. The tourism research community has provided an increasing number of works that analyse destinations from a n...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	29,477
2103.10698	AutoTune: Controller Tuning for High-Speed Flight	Due to noisy actuation and external disturbances, tuning controllers for high-speed flight is very challenging. In this paper, we ask the following questions: How sensitive are controllers to tuning when tracking high-speed maneuvers? What algorithms can we use to automatically tune them? To answer the first question, ...	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	225,545
2101.07540	A synthetic biology approach for the design of genetic algorithms with bacterial agents	Bacteria have been a source of inspiration for the design of evolutionary algorithms. At the beginning of the 20th century synthetic biology was born, a discipline whose goal is the design of biological systems that do not exist in nature, for example, programmable synthetic bacteria. In this paper, we introduce as a n...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	true	false	false	216,063
2406.11633	DocGenome: An Open Large-scale Scientific Document Benchmark for Training and Testing Multi-modal Large Language Models	Scientific documents record research findings and valuable human knowledge, comprising a vast corpus of high-quality data. Leveraging multi-modality data extracted from these documents and assessing large models' abilities to handle scientific document-oriented tasks is therefore meaningful. Despite promising advanceme...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	464,972
1411.0001	Prognosis of Anterior Cruciate Ligament (ACL) Reconstruction: A Data Driven Approach	Individuals who suffer anterior cruciate ligament (ACL) injury are at higher risk of developing knee osteoarthritis (OA) and almost 50% display symptoms 10 to 20 years post injury. Anterior cruciate ligament reconstruction (ACLR) often does not protect against knee OA development. Accordingly, a multiscale formulation ...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	37,200
2201.10471	GIU-GANs: Global Information Utilization for Generative Adversarial Networks	In recent years, with the rapid development of artificial intelligence, image generation based on deep learning has dramatically advanced. Image generation based on Generative Adversarial Networks (GANs) is a promising study. However, since convolutions are limited by spatial-agnostic and channel-specific, features ext...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	277,006
2210.12497	Deep Linear Networks for Matrix Completion -- An Infinite Depth Limit	The deep linear network (DLN) is a model for implicit regularization in gradient based optimization of overparametrized learning architectures. Training the DLN corresponds to a Riemannian gradient flow, where the Riemannian metric is defined by the architecture of the network and the loss function is defined by the le...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	325,769
1203.2860	Receding Horizon Temporal Logic Control for Finite Deterministic Systems	This paper considers receding horizon control of finite deterministic systems, which must satisfy a high level, rich specification expressed as a linear temporal logic formula. Under the assumption that time-varying rewards are associated with states of the system and they can be observed in real-time, the control obje...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	14,860
2207.06256	Image warp preserving content intensity	An accurate method for warping images is presented. Differently from most commonly used techniques, this method guarantees the conservation of the intensity of the transformed image, evaluated as the sum of its pixel values over the whole image or over corresponding transformed subregions of it. Such property is mandat...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	307,824
2103.06541	Affect2MM: Affective Analysis of Multimedia Content Using Emotion Causality	We present Affect2MM, a learning method for time-series emotion prediction for multimedia content. Our goal is to automatically capture the varying emotions depicted by characters in real-life human-centric situations and behaviors. We use the ideas from emotion causation theories to computationally model and determine...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	true	224,341
1008.1710	Introduction to the 26th International Conference on Logic Programming Special Issue	This is the preface to the 26th International Conference on Logic Programming Special Issue	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	7,243
2402.01116	Scalable Multi-modal Model Predictive Control via Duality-based Interaction Predictions	We propose a hierarchical architecture designed for scalable real-time Model Predictive Control (MPC) in complex, multi-modal traffic scenarios. This architecture comprises two key components: 1) RAID-Net, a novel attention-based Recurrent Neural Network that predicts relevant interactions along the MPC prediction hori...	false	false	false	false	false	false	true	true	false	false	true	false	false	false	false	false	false	false	425,868
1507.04213	Soft Pilot Reuse and Multi-Cell Block Diagonalization Precoding for Massive MIMO Systems	The users at cell edge of a massive multiple-input multiple-output (MIMO) system suffer from severe pilot contamination, which leads to poor quality of service (QoS). In order to enhance the QoS for these edge users, soft pilot reuse (SPR) combined with multi-cell block diagonalization (MBD) precoding are proposed. Spe...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	45,149
2002.05654	Summarizing the performances of a background subtraction algorithm measured on several videos	There exist many background subtraction algorithms to detect motion in videos. To help comparing them, datasets with ground-truth data such as CDNET or LASIESTA have been proposed. These datasets organize videos in categories that represent typical challenges for background subtraction. The evaluation procedure promote...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	163,965
2212.09132	JEMMA: An Extensible Java Dataset for ML4Code Applications	Machine Learning for Source Code (ML4Code) is an active research field in which extensive experimentation is needed to discover how to best use source code's richly structured information. With this in mind, we introduce JEMMA, an Extensible Java Dataset for ML4Code Applications, which is a large-scale, diverse, and hi...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	337,012
2310.10250	Leveraging Topological Maps in Deep Reinforcement Learning for Multi-Object Navigation	This work addresses the challenge of navigating expansive spaces with sparse rewards through Reinforcement Learning (RL). Using topological maps, we elevate elementary actions to object-oriented macro actions, enabling a simple Deep Q-Network (DQN) agent to solve otherwise practically impossible environments.	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	400,149
1901.09575	Enhancing Quality for VVC Compressed Videos by Jointly Exploiting Spatial Details and Temporal Structure	In this paper, we propose a quality enhancement network of versatile video coding (VVC) compressed videos by jointly exploiting spatial details and temporal structure (SDTS). The proposed network consists of a temporal structure fusion subnet and a spatial detail enhancement subnet. The former subnet is used to estimat...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	119,789
1806.09819	Limited Evaluation Evolutionary Optimization of Large Neural Networks	Stochastic gradient descent is the most prevalent algorithm to train neural networks. However, other approaches such as evolutionary algorithms are also applicable to this task. Evolutionary algorithms bring unique trade-offs that are worth exploring, but computational demands have so far restricted exploration to smal...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	101,433
2501.04315	RoRA: Efficient Fine-Tuning of LLM with Reliability Optimization for Rank Adaptation	Fine-tuning helps large language models (LLM) recover degraded information and enhance task performance. Although Low-Rank Adaptation (LoRA) is widely used and effective for fine-tuning, we have observed that its scaling factor can limit or even reduce performance as the rank size increases. To address this issue, we p...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	523,179
2501.19191	Secured Communication Schemes for UAVs in 5G: CRYSTALS-Kyber and IDS	This paper introduces a secure communication architecture for Unmanned Aerial Vehicles (UAVs) and ground stations in 5G networks, addressing critical challenges in network security. The proposed solution integrates the Advanced Encryption Standard (AES) with Elliptic Curve Cryptography (ECC) and CRYSTALS-Kyber for key ...	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	false	false	false	529,050
2306.03271	Volumetric medical image segmentation through dual self-distillation in U-shaped networks	U-shaped networks and its variants have demonstrated exceptional results for medical image segmentation. In this paper, we propose a novel dual self-distillation (DSD) framework in U-shaped networks for volumetric medical image segmentation. DSD distills knowledge from the ground-truth segmentation labels to the decode...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	371,256
2412.13641	Learning to Control an Android Robot Head for Facial Animation	The ability to display rich facial expressions is crucial for human-like robotic heads. While manually defining such expressions is intricate, there already exist approaches to automatically learn them. In this work one such approach is applied to evaluate and control a robot head different from the one in the original...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	518,380
2205.14325	Feature subset selection for kernel SVM classification via mixed-integer optimization	We study the mixed-integer optimization (MIO) approach to feature subset selection in nonlinear kernel support vector machines (SVMs) for binary classification. First proposed for linear regression in the 1970s, this approach has recently moved into the spotlight with advances in optimization algorithms and computer ha...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	299,315
1710.06202	Deep Gaussian Covariance Network	The correlation length-scale next to the noise variance are the most used hyperparameters for the Gaussian processes. Typically, stationary covariance functions are used, which are only dependent on the distances between input points and thus invariant to the translations in the input space. The optimization of the hyp...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	82,735
2207.07238	Emotion Recognition in Conversation using Probabilistic Soft Logic	Creating agents that can both appropriately respond to conversations and understand complex human linguistic tendencies and social cues has been a long standing challenge in the NLP community. A recent pillar of research revolves around emotion recognition in conversation (ERC); a sub-field of emotion recognition that ...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	308,141
2309.02569	A Robust Localization Solution for an Uncrewed Ground Vehicle in Unstructured Outdoor GNSS-Denied Environments	This work addresses the challenge of developing a localization system for an uncrewed ground vehicle (UGV) operating autonomously in unstructured outdoor Global Navigation Satellite System (GNSS)-denied environments. The goal is to enable accurate mapping and long-range navigation with practical applications in domains...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	390,084
2302.10835	A General-Purpose Transferable Predictor for Neural Architecture Search	Understanding and modelling the performance of neural architectures is key to Neural Architecture Search (NAS). Performance predictors have seen widespread use in low-cost NAS and achieve high ranking correlations between predicted and ground truth performance in several NAS benchmarks. However, existing predictors are...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	346,969
cs/0411006	Capacity Achieving Code Constructions for Two Classes of (d,k) Constraints	In this paper, we present two low complexity algorithms that achieve capacity for the noiseless (d,k) constrained channel when k=2d+1, or when k-d+1 is not prime. The first algorithm, called symbol sliding, is a generalized version of the bit flipping algorithm introduced by Aviran et al. [1]. In addition to achieving ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	538,385
2112.05924	Cascode Cross-Coupled Stage High-Speed Dynamic Comparator in 65 nm CMOS	Dynamic comparators are the core of high-speed, high-resolution analog-to-digital converters (ADCs) used for communication applications. Most of the dynamic comparators attain high-speed operation only for sufficiently high input difference voltages. The comparator performance degrades at small input difference voltage...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	271,004
2402.18147	A Lightweight Low-Light Image Enhancement Network via Channel Prior and Gamma Correction	Human vision relies heavily on available ambient light to perceive objects. Low-light scenes pose two distinct challenges: information loss due to insufficient illumination and undesirable brightness shifts. Low-light image enhancement (LLIE) refers to image enhancement technology tailored to handle this scenario. We i...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	433,308
2310.15757	Do Differences in Values Influence Disagreements in Online Discussions?	Disagreements are common in online discussions. Disagreement may foster collaboration and improve the quality of a discussion under some conditions. Although there exist methods for recognizing disagreement, a deeper understanding of factors that influence disagreement is lacking in the literature. We investigate a hyp...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	402,438
2112.04126	FreeTalky: Don't Be Afraid! Conversations Made Easier by a Humanoid Robot using Persona-based Dialogue	We propose a deep learning-based foreign language learning platform, named FreeTalky, for people who experience anxiety dealing with foreign languages, by employing a humanoid robot NAO and various deep learning models. A persona-based dialogue system that is embedded in NAO provides an interesting and consistent multi...	false	false	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	270,429
1304.5961	Backdoors to Abduction	Abductive reasoning (or Abduction, for short) is among the most fundamental AI reasoning methods, with a broad range of applications, including fault diagnosis, belief revision, and automated planning. Unfortunately, Abduction is of high computational complexity; even propositional Abduction is \Sigma_2^P-complete and ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	24,133
2411.01545	Towards Small Object Editing: A Benchmark Dataset and A Training-Free Approach	A plethora of text-guided image editing methods has recently been developed by leveraging the impressive capabilities of large-scale diffusion-based generative models especially Stable Diffusion. Despite the success of diffusion models in producing high-quality images, their application to small object generation has b...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	505,121
2502.01510	Grid-based exoplanet atmospheric mass loss predictions through neural network	The fast and accurate estimation of planetary mass-loss rates is critical for planet population and evolution modelling. We use machine learning (ML) for fast interpolation across an existing large grid of hydrodynamic upper atmosphere models, providing mass-loss rates for any planet inside the grid boundaries with sup...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	529,886
1506.04571	A community role approach to assess social capitalists visibility in the Twitter network	In the context of Twitter, social capitalists are specific users trying to increase their number of followers and interactions by any means. These users are not healthy for the service, because they are either spammers or real users flawing the notions of influence and visibility. Studying their behavior and understand...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	44,182
2407.16601	Considering dynamical synergy and integrated information; the unusual case of minimum mutual information	This brief note considers the problem of estimating temporal synergy and integrated information in dyadic dynamical processes. One of the standard estimators of dynamic synergy is based on the minimal mutual information between sets of elements, however, despite it's increasingly widespread use, the mathematical featur...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	475,651
1812.11859	A discrete version of CMA-ES	Modern machine learning uses more and more advanced optimization techniques to find optimal hyper parameters. Whenever the objective function is non-convex, non continuous and with potentially multiple local minima, standard gradient descent optimization methods fail. A last resource and very different method is to ass...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	117,644
1304.0878	C Language Extensions for Hybrid CPU/GPU Programming with StarPU	Modern platforms used for high-performance computing (HPC) include machines with both general-purpose CPUs, and "accelerators", often in the form of graphical processing units (GPUs). StarPU is a C library to exploit such platforms. It provides users with ways to define "tasks" to be executed on CPUs or GPUs, along wit...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	23,420
1609.02613	Why is Differential Evolution Better than Grid Search for Tuning Defect Predictors?	Context: One of the black arts of data mining is learning the magic parameters which control the learners. In software analytics, at least for defect prediction, several methods, like grid search and differential evolution (DE), have been proposed to learn these parameters, which has been proved to be able to improve t...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	60,758
2110.02932	Machine Learning Practices Outside Big Tech: How Resource Constraints Challenge Responsible Development	Practitioners from diverse occupations and backgrounds are increasingly using machine learning (ML) methods. Nonetheless, studies on ML Practitioners typically draw populations from Big Tech and academia, as researchers have easier access to these communities. Through this selection bias, past research often excludes t...	false	false	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	259,309
1008.1977	Guessing Revisited: A Large Deviations Approach	The problem of guessing a random string is revisited. A close relation between guessing and compression is first established. Then it is shown that if the sequence of distributions of the information spectrum satisfies the large deviation property with a certain rate function, then the limiting guessing exponent exists...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	7,251
2206.13742	The COVID-19 Pandemic on the Turkish Twittersphere	With the increase in the time spent at home, social media platforms' role has become an integral part of the public discussion in the COVID-19 period. Individuals use social media platforms to express their emotions, interact, and engage in public debate. Therefore, it is essential to analyze social media platforms for...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	305,068
2303.09390	On the Interplay Between Misspecification and Sub-optimality Gap in Linear Contextual Bandits	We study linear contextual bandits in the misspecified setting, where the expected reward function can be approximated by a linear function class up to a bounded misspecification level $\zeta>0$. We propose an algorithm based on a novel data selection scheme, which only selects the contextual vectors with large uncerta...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	352,024
1204.4539	Supervised Feature Selection in Graphs with Path Coding Penalties and Network Flows	We consider supervised learning problems where the features are embedded in a graph, such as gene expressions in a gene network. In this context, it is of much interest to automatically select a subgraph with few connected components; by exploiting prior knowledge, one can indeed improve the prediction performance or o...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	15,599
1304.8052	Registration of Images with Outliers Using Joint Saliency Map	Mutual information (MI) is a popular similarity measure for image registration, whereby good registration can be achieved by maximizing the compactness of the clusters in the joint histogram. However, MI is sensitive to the "outlier" objects that appear in one image but not the other, and also suffers from local and bi...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	24,309
2401.05235	A Survey on Optimization Studies of Group Centrality Metrics	Centrality metrics have become a popular concept in network science and optimization. Over the years, centrality has been used to assign importance and identify influential elements in various settings, including transportation, infrastructure, biological, and social networks, among others. That said, most of the liter...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	420,691
2409.02337	Coaching a Robotic Sonographer: Learning Robotic Ultrasound with Sparse Expert's Feedback	Ultrasound is widely employed for clinical intervention and diagnosis, due to its advantages of offering non-invasive, radiation-free, and real-time imaging. However, the accessibility of this dexterous procedure is limited due to the substantial training and expertise required of operators. The robotic ultrasound (RUS...	false	false	false	false	true	false	false	true	false	false	false	true	false	false	false	false	false	false	485,656
1408.1260	Unstable markup: A template-based information extraction from web sites with unstable markup	This paper presents results of a work on crawling CEUR Workshop proceedings web site to a Linked Open Data (LOD) dataset in the framework of ESWC 2014 Semantic Publishing Challenge 2014. Our approach is based on using an extensible template-dependent crawler and DBpedia for linking extracted entities, such as the names...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	true	35,148
2408.00647	Counterclockwise Dissipativity, Potential Games and Evolutionary Nash Equilibrium Learning	We use system-theoretic passivity methods to study evolutionary Nash equilibria learning in large populations of agents engaged in strategic, non-cooperative interactions. The agents follow learning rules (rules for short) that capture their strategic preferences and a payoff mechanism ascribes payoffs to the available...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	477,917
2004.13717	Informational Space of Meaning for Scientific Texts	In Natural Language Processing, automatic extracting the meaning of texts constitutes an important problem. Our focus is the computational analysis of meaning of short scientific texts (abstracts or brief reports). In this paper, a vector space model is developed for quantifying the meaning of words and texts. We intro...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	174,644
1006.1692	Measuring interesting rules in Characteristic rule	Finding interesting rule in the sixth strategy step about threshold control on generalized relations in attribute oriented induction, there is possibility to select candidate attribute for further generalization and merging of identical tuples until the number of tuples is no greater than the threshold value, as implem...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	true	false	6,729
2111.12853	Domain Prompt Learning for Efficiently Adapting CLIP to Unseen Domains	Domain generalization (DG) is a difficult transfer learning problem aiming to learn a generalizable model for unseen domains. Recent foundation models (FMs) are robust to many distribution shifts and, therefore, should substantially improve the performance of DG. In this work, we study generic ways to adopt CLIP, a Vis...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	268,094
cs/0406004	Application of Business Intelligence In Banks (Pakistan)	The financial services industry is rapidly changing. Factors such as globalization, deregulation, mergers and acquisitions, competition from non-financial institutions, and technological innovation, have forced companies to re-think their business.Many large companies have been using Business Intelligence (BI) computer...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	538,228
2409.06189	MyGo: Consistent and Controllable Multi-View Driving Video Generation with Camera Control	High-quality driving video generation is crucial for providing training data for autonomous driving models. However, current generative models rarely focus on enhancing camera motion control under multi-view tasks, which is essential for driving video generation. Therefore, we propose MyGo, an end-to-end framework for ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	487,031
2411.09429	AI-driven inverse design of materials: Past, present and future	The discovery of advanced materials is the cornerstone of human technological development and progress. The structures of materials and their corresponding properties are essentially the result of a complex interplay of multiple degrees of freedom such as lattice, charge, spin, symmetry, and topology. This poses signif...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	508,243
2401.11188	Fast and Exact Enumeration of Deep Networks Partitions Regions	One fruitful formulation of Deep Networks (DNs) enabling their theoretical study and providing practical guidelines to practitioners relies on Piecewise Affine Splines. In that realm, a DN's input-mapping is expressed as per-region affine mapping where those regions are implicitly determined by the model's architecture...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	422,903
1407.0506	The Design and Implementation of an ANN-based Non-linearity Compensator of LVDT Sensor	Linear variable differential transformer (LVDT) sensors are used in engineering applications due to their fine-grained measurements. However, these sensors exhibit non-linear input-output characteristics, which decrease the reliability of the sensing system. The contribution of this article is three-fold. First, it pro...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	34,339
1110.5092	Geometry of the 3-user MIMO interference channel	This paper studies vector space interference alignment for the three-user MIMO interference channel with no time or frequency diversity. The main result is a characterization of the feasibility of interference alignment in the symmetric case where all transmitters have M antennas and all receivers have N antennas. If N...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	12,747
2104.10447	A Meta-Learning Approach for Medical Image Registration	Non-rigid registration is a necessary but challenging task in medical imaging studies. Recently, unsupervised registration models have shown good performance, but they often require a large-scale training dataset and long training times. Therefore, in real world application where only dozens to hundreds of image pairs ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	231,585
2205.06485	Modeling Human Behavior Part I -- Learning and Belief Approaches	There is a clear desire to model and comprehend human behavior. Trends in research covering this topic show a clear assumption that many view human reasoning as the presupposed standard in artificial reasoning. As such, topics such as game theory, theory of mind, machine learning, etc. all integrate concepts which are ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	296,254
2308.11263	Distributed Energy Resource Management: All-Time Resource-Demand Feasibility, Delay-Tolerance, Nonlinearity, and Beyond	In this work, we propose distributed and networked energy management scenarios to optimize the production and reservation of energy among a set of distributed energy nodes. In other words, the idea is to optimally allocate the generated and reserved powers based on nodes' local cost gradient information while meeting t...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	387,074
2303.05497	Learning Stationary Markov Processes with Contrastive Adjustment	We introduce a new optimization algorithm, termed contrastive adjustment, for learning Markov transition kernels whose stationary distribution matches the data distribution. Contrastive adjustment is not restricted to a particular family of transition distributions and can be used to model data in both continuous and d...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	350,486
2502.12381	Linear Diffusion Networks: Harnessing Diffusion Processes for Global Interactions	Diffusion kernels capture global dependencies. We present Linear Diffusion Networks (LDNs), a novel architecture that reinterprets sequential data processing as a unified diffusion process. Our model integrates adaptive diffusion modules with localized nonlinear updates and a diffusion-inspired attention mechanism. Thi...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	534,833
2310.12996	Zero-shot Learning of Drug Response Prediction for Preclinical Drug Screening	Conventional deep learning methods typically employ supervised learning for drug response prediction (DRP). This entails dependence on labeled response data from drugs for model training. However, practical applications in the preclinical drug screening phase demand that DRP models predict responses for novel compounds...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	401,240
1902.09839	Capsule Neural Network based Height Classification using Low-Cost Automotive Ultrasonic Sensors	High performance ultrasonic sensor hardware is mainly used in medical applications. Although, the development in automotive scenarios is towards autonomous driving, the ultrasonic sensor hardware still stays low-cost and low-performance, respectively. To overcome the strict hardware limitations, we propose to use capsu...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	122,520
1908.05519	Cosmological N-body simulations: a challenge for scalable generative models	Deep generative models, such as Generative Adversarial Networks (GANs) or Variational Autoencoders (VAs) have been demonstrated to produce images of high visual quality. However, the existing hardware severely limits the size of the images that can be generated. The rapid growth of high dimensional data in many fields ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	141,737
2011.11721	Siamese Tracking with Lingual Object Constraints	Classically, visual object tracking involves following a target object throughout a given video, and it provides us the motion trajectory of the object. However, for many practical applications, this output is often insufficient since additional semantic information is required to act on the video material. Example app...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	207,910
2209.07667	Can There be Art Without an Artist?	Generative AI based art has proliferated in the past year, with increasingly impressive use cases from generating fake human faces to the creation of systems that can generate thousands of artistic images from text prompts - some of these images have even been "good" enough to win accolades from qualified judges. In th...	false	false	false	false	true	false	true	false	false	false	false	false	false	true	false	false	false	false	317,837
2103.12545	MetaHDR: Model-Agnostic Meta-Learning for HDR Image Reconstruction	Capturing scenes with a high dynamic range is crucial to reproducing images that appear similar to those seen by the human visual system. Despite progress in developing data-driven deep learning approaches for converting low dynamic range images to high dynamic range images, existing approaches are limited by the assum...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	226,215
2402.06629	Towards the mathematical foundation of the minimum enclosing ball and related problems	Theoretical background is provided towards the mathematical foundation of the minimum enclosing ball problem. This problem concerns the determination of the unique spherical surface of smallest radius enclosing a given bounded set in the d-dimensional Euclidean space. The study of several problems that are similar or r...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	428,368

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